I trained a model in AZURE ML. Now i want to use that model in my ios app to predict the output .
How to download the model from AZURE and use it my swift code.
As far as I know, the model could run in Azure Machine Learning Studio.It seems that you are unable to download it, the model could do nothing outside of Azure ML.
Here is a similar post for you to refer, I have also tried #Ahmet's
method, but result is like #mrjrdnthms says.
Related
Basically title. The Azure documentation for v2 is constantly getting updated, and as of now i have no resource to find out how you can register a pre-trained model from SentenceTransformers on AzureML for future use in endpoints. The library is based on Pytorch, but so far I've had no luck in using MLFlow(mentioned in the docs) to register it.
I don't have much code to show, so any help whatsoever would be appreciated.
With MLFlow, you have to first save or log your model before you can register it. But with log_model you can do both in one step
mlflow.pytorch.log_model(model, "my_model_path", registered_model_name="fancy")
Then it is easiest to deploy it from the AzureML Studio:
In the latest version of the Anomaly Detection Service by Azure which supports the Multi-variate Cognitive Service, we need to train a model and then consume it.
The quickstart documentation for Python mentions a few libraries which are not getting imported:
from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo
Both these libraries are throwing import errors.
How can we use the Multivariate Anomaly Detection Service using the Python SDK?
This error was with version azure-ai-anomalydetector==3.0.0b2. With version azure-ai-anomalydetector==3.0.0b3, this has been addressed.
The problem is because of the change of the response format recently. To solve that issue, you can change the line with error to
model_status = self.ad_client.get_multivariate_model(trained_model_id).model_info.status
Under the openvinotoolkit is a repo for training deepReID and there are a number of projects such as training a model for person attributes
Is this the codebase that was used to train the pre-trained models provided with OpenVino?
Is it possible to provide a script to show how to obtain the pre-trained models using the code?
The person attribute uses a dataset with more attributes than provided by the pre-trained model. Is there an updated model using this repo?
Deep-object-reid uses Torchreid. Torchreid is a library for deep-learning person re-identification, written in PyTorch. This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
You can download the pretrained models from Open Model Zoo by going to the directory:
openvino/deployment_tools/open_model_zoo/tools/downloader
Then, run the following command to download pre-trained models:
./downloader.py --name <model_name>
For more details, please visit this page.
This repo is forked from KaiyangZhou/deep-person-reid.
i am developing using ML-Kit. But there is a path problem. It's the same as the example, but what's wrong with it? Like this, enter image description here
If you plan to use ML Kit, you can ignore that message and everything should works fine using ML Kit.
ML Model Binding is another feature that uses codegen to generate a wrapper java class for your model. It's works best when your model has metadata inside.
Here list models with metadata if you want to try.
I hope I am not missing something obvious here. I am using the new azure ml studio designer. I am able to use to create datasets, train models and use them just fine.
azure ml studio allows creation of Jupyter notebooks (also) and use them to do machine learning. I am able to do that too.
So, now, I am wondering, can I build my ML pipeline/experiment in ML studio designer, and once it is in good shape, export it as a python and jupyter notebook? then, use it in the same designer provided notebook option or may be use it locally?
This is not currently supported, but I am 80% sure it is in the roadmap.
An alternative would be to use the SDK to create the same pipeline using ModuleStep where I believe you can reference a Designer Module by its name to use it like a PythonScriptStep
The export Designer graph to notebook is in our roadmap. For now, please take a look at the ModuleStep in SDK and let us know if you have any questions.
Thanks,
Lu Zhang | Senior Program Manager | Azure Machine Learning
Here are the instructions to Use the studio to deploy models trained in the designer - Azure Machine Learning | Microsoft Docs and document that explains how we can get access to score.py and conda_env.yaml files under Output + logs tab for Train module.